Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction in applications throughout science and engineering. However, the principal components (PCs) can ...sometimes be difficult to interpret, because they are linear combinations of all the original variables. To facilitate interpretation, sparse PCA produces modified PCs with sparse loadings, i.e. loadings with very few non-zero elements. In this paper, we propose a new sparse PCA method, namely
sparse PCA via regularized SVD (sPCA-rSVD). We use the connection of PCA with singular value decomposition (SVD) of the data matrix and extract the PCs through solving a low rank matrix approximation problem. Regularization penalties are introduced to the corresponding minimization problem to promote sparsity in PC loadings. An efficient iterative algorithm is proposed for computation. Two tuning parameter selection methods are discussed. Some theoretical results are established to justify the use of sPCA-rSVD when only the data covariance matrix is available. In addition, we give a modified definition of variance explained by the sparse PCs. The sPCA-rSVD provides a uniform treatment of both classical multivariate data and high-dimension-low-sample-size (HDLSS) data. Further understanding of sPCA-rSVD and some existing alternatives is gained through simulation studies and real data examples, which suggests that sPCA-rSVD provides competitive results.
Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of ...analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.
The effective management of call centers is a challenging task, mainly because managers consistently face considerable uncertainty. One important source of this uncertainty is the call arrival rate, ...which is typically time-varying, stochastic, dependent across time periods and call types, and often affected by external events. The accurate modeling and forecasting of future call arrival volumes is a complicated issue which is critical for making important operational decisions, such as staffing and scheduling, in the call center. In this paper, we review the existing literature on modeling and forecasting call arrivals. We also discuss the key issues for the building of good statistical arrival models. In addition, we evaluate the forecasting accuracy of selected models in an empirical study with real-life call center data. We conclude with a summary of possible future research directions in this important field.
To develop and validate a risk score (acute ischemic stroke-associated pneumonia score AIS-APS) for predicting in-hospital stroke-associated pneumonia (SAP) after AIS.
The AIS-APS was developed based ...on the China National Stroke Registry, in which eligible patients were randomly classified into derivation (60%) and internal validation cohort (40%). External validation was performed using the prospective Chinese Intracranial Atherosclerosis Study. Independent predictors of in-hospital SAP after AIS were obtained using multivariable logistic regression, and β-coefficients were used to generate point scoring system of the AIS-APS. The area under the receiver operating characteristic curve and the Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration, respectively.
The overall in-hospital SAP after AIS was 11.4%, 11.3%, and 7.3% in the derivation (n=8820), internal (n=5882) and external (n=3037) validation cohort, respectively. A 34-point AIS-APS was developed from the set of independent predictors including age, history of atrial fibrillation, congestive heart failure, chronic obstructive pulmonary disease and current smoking, prestroke dependence, dysphagia, admission National Institutes of Health Stroke Scale score, Glasgow Coma Scale score, stroke subtype (Oxfordshire), and blood glucose. The AIS-APS showed good discrimination (area under the receiver operating characteristic curve) in the internal (0.785; 95% confidence interval, 0.766-0.803) and external (0.792; 95% confidence interval, 0.761-0.823) validation cohort. The AIS-APS was well calibrated (Hosmer-Lemeshow test) in the internal (P=0.22) and external (P=0.30) validation cohort. When compared with 3 prior scores, the AIS-APS showed significantly better discrimination with regard to in-hospital SAP after AIS (all P<0.0001).
The AIS-APS is a valid risk score for predicting in-hospital SAP after AIS.
Summary Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data ...matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity-inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets.
Sparse Principal Component Analysis (PCA) methods are efficient tools to reduce the dimension (or number of variables) of complex data. Sparse principal components (PCs) are easier to interpret than ...conventional PCs, because most loadings are zero. We study the asymptotic properties of these sparse PC directions for scenarios with fixed sample size and increasing dimension (i.e. High Dimension, Low Sample Size (HDLSS)). We consider the previously studied single spike covariance model and assume in addition that the maximal eigenvector is sparse. We extend the existing HDLSS asymptotic consistency and strong inconsistency results of conventional PCA in an entirely new direction. We find a large set of sparsity assumptions under which sparse PCA is still consistent even when conventional PCA is strongly inconsistent. The consistency of sparse PCA is characterized along with rates of convergence. Furthermore, we clearly identify the mathematical boundaries of the sparse PCA consistency, by showing strong inconsistency for an oracle version of sparse PCA beyond the consistent region, as well as its inconsistency on the boundaries of the consistent region. Simulation studies are performed to validate the asymptotic results in finite samples.
BACKGROUND AND PURPOSE—Medical complications are common among patients with stroke. However, little is known about the potential interrelationship among them. In the present study, we aimed to ...investigate the association between common in-hospital medical complications after acute ischemic stroke (AIS) and spontaneous intracerebral hemorrhage (ICH).
METHODS—We analyzed patients enrolled in the China National Stroke Registry from 2007 to 2008. The occurrence of 11 common stroke-associated medical complications during acute hospitalization was prospectively registered. Multivariable analysis using generalized estimation equation was performed to assess association between medical complications in AIS and ICH cohort, respectively.
RESULTS—A total of 14 702 patients with AIS and 5221 patients with ICH were enrolled. The median age was 65 years (interquartile range, 55–74 years), and 38.1% were female. The median length of hospital stay was 14 days (interquartile range, 10–20 days) for AIS and 18 days (interquartile range, 11–26 days) for ICH. Pneumonia was the most common medical complication after AIS (11.4%) and ICH (16.8%). In the AIS cohort, after adjusting for potential confounders, pneumonia was significantly associated with development of gastrointestinal bleeding (adjusted odds ratio OR, 8.35; 95% confidence interval CI, 6.27–11.1; P<0.001), decubitus ulcer (adjusted OR, 5.31; 95% CI, 3.39–8.31; P<0.001), deep vein thrombosis (adjusted OR, 4.27; 95% CI, 2.41–7.59; P<0.001), epileptic seizure (adjusted OR, 3.96; 95% CI, 2.67–5.88; P<0.001), urinary tract infection (adjusted OR, 3.34; 95% CI, 2.73–4.10; P<0.001), atrial fibrillation/flutter (adjusted OR, 3.17; 95% CI, 2.58–3.90; P<0.001), and recurrent stroke (adjusted OR, 2.65; 95% CI, 2.07–3.40; P<0.001). Similar significant association between pneumonia and development of several nonpneumonia medical complications was verified in ICH cohort as well.
CONCLUSIONS—Pneumonia is closely associated with the development of several nonpneumonia medical complications after AIS and ICH.
Vehicle-bridge coupling analysis has always been a hot issue in bridge structure research. Model polycondensation technology is an effective calculation method for solving large-scale structural ...dynamic analysis. In this paper, three different model polycondensation theory was described, and based on the model polycondensation theory, a new vehicle-bridge coupling calculation idea is proposed, the corresponding calculation program is compiled by adopting MATLAB. In order to verify the theory and program, a vehicle-bridge model test was designed, and the 3-D numerical model of the vehicle and the bridge was established respectively. Modal analysis and the vehicle-bridge coupled transient analysis were carried out. The correctness of the calculation theory and program was verified by comparing the experimental values with the numerical simulation values, and the advantages and disadvantages of different methods are discussed by the comparation of the solving accuracy and time consumption.
A survey of high dimension low sample size asymptotics Aoshima, Makoto; Shen, Dan; Shen, Haipeng ...
Australian & New Zealand journal of statistics,
March 2018, 2018-Mar, 2018-03-00, 20180301, Letnik:
60, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Summary
Peter Hall's work illuminated many aspects of statistical thought, some of which are very well known including the bootstrap and smoothing. However, he also explored many other lesser known ...aspects of mathematical statistics. This is a survey of one of those areas, initiated by a seminal paper in 2005, on high dimension low sample size asymptotics. An interesting characteristic of that first paper, and of many of the following papers, is that they contain deep and insightful concepts which are frequently surprising and counter‐intuitive, yet have mathematical underpinnings which tend to be direct and not difficult to prove.
This is a survey of HDLSS asymptotics. We introduce geometric representations, classical PCA, new PCAs, classifications and hypothesis tests in HDLSS settings
We review and discuss the key issues in building statistical models for the call arrival process in telephone call centers, and then we survey and compare various types of models proposed so far. ...These models are used both for simulation and to forecast incoming call volumes to make staffing decisions and build (or update) work schedules for agents who answer those calls. Commercial software and call center managers usually base their decisions solely on point forecasts, given in the form of mathematical expectations (conditional on current information), but distributional forecasts, which come in the form of (conditional) probability distributions, are generally more useful, in particular in the context of simulation. Building realistic models is not simple, because arrival rates are themselves stochastic, time-dependent, dependent across time periods and across call types, and are often affected by external events. As an illustration, we evaluate the forecasting accuracy of selected models in an empirical study with real-life call center data.
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